Data Visualisation Link (Tableau Public) - https://public.tableau.com/profile/xinyue.bai#!/vizhome/ofStronglyAgree-gettingCOVID/DatavizMakeover2?publish=yes
1. Background Information
For this visualisation makeover, I have used data from Imperial College London YouGov Covid 19 Behaviour Tracker Data Hub. This data gathers global insights on people’s behaviours in response to COVID-19 covering 29 countries, in the form of survey questionnaire. In particular, this post is interested in exploring the willingness of the public on COVID-vaccination. In this blog, I will makeover visualisation on vaccine willingness done by one of the research scientists, by examining the following 3 survey questions in the context of different gender and employment status:
1. If a Covid-19 vaccine were made available to me this week, I would definitely get it.
2. I am worried about getting COVID19.
3. I am worried about potential side effects of a COVID19 vaccine.
2. Critiques and Suggestions
2.1 Clarity
| SN | Critique | Suggestion |
|---|---|---|
| 1 | Visualisation on the left is intended to show “which country is more pro-vaccine”, by computing the percentage of each response and combining them into a percentage stacked bar chart. However, for each country, it’s not easy to see the proportion of pro-vaccine and compare it among different countries. | Sort countries by proportion of pro-vaccine responses in descending order. |
| 2 | Moreover, scaling each bar into the same height is not clear enough to compare the difference between pro-vaccine responses and anti-vaccine responses. | Place neutral responses at centre 0, negative value showing proportion of disagree responses and positive value showing proportion of agree responses. |
| 3 | It’s hard to distinguish bars having the same length. | Add value label on each bar. |
| 4 | Legend is not very clear, i.e. what does 2, 3, 4 represent specifically? | Change 2, 3, 4 to 2 – Agree, 3 – I don’t know, 4 – Disagree respectively. |
| 5 | Visualisation on the right is generally straightforward and clear. However, the simple percentage does not reflect statistical measures, how much you can expect your service results to reflect the view from the overall population. For example, if we have a high proportion of strongly agree responses with a wide margin of error, then this survey result is not very reliable. | Calculate confidence interval for each value to get a more comprehensive view of the survey results. |
| 6 | Moreover, this visualisation is not convincing enough in a way that only high level view of the survey results is presented and insights from different angles are not revealed. For example, by examining deeper into gender level, males are generally more willing to get vaccinated than females respondents. | Explore survey results from various perspectives, such as gender and employment status, and the relation between other survey questions. |
2.2 Aesthetics
| SN | Critique | Suggestion |
|---|---|---|
| 1 | It’s redundant and distractive to use five different colours for each response in the left visualisation, making it hard to view the survey results. | Use the same colour for the same group (1.agree 2.neutral 3. disagree) with different hue level. For example, dark green for strongly agree, light green for agree. | Use the same colour for the same group (1.agree 2.neutral 3. disagree) with different hue level. For example, dark green for strongly agree, light green for agree. |
| 2 | The x-axis of two visualisations are not consistent, the first plot has no decimal place whereas the second plot has 2 decimal places. | Make them consistent. |
| 3 | Generally good axis marks in twenties and grid lines to facilitate easy readings, clear use of fonts, font sizes and layout with very straightforward titles. | Follow and format to ensure so. |
3. Proposed Design
3.1 Sketch
3.2 Advantages of Proposed Design
The first visualisation:
1. Clearly separating the pro-vaccine and anti-vaccine responses into postive and negative x-axis makes audiences easier to detect difference between two types of responses and difference between countries.
2. Sorting rows according to % of pro-vaccine responses answers the question of “which country is more pro-vaccine” in a more clear way.
The second visualisation:
Using confidence interval gives audiences a better sense of how reliable is the result.
The third visualisation:
Looking into result of survey’s questions vac2.1(worry about getting COVID) and vac2.2(worry about potential side effect of vaccine) help audiences better understand the public willingness on Covid-19 vaccination and the potential reasons why the public agrees or disagrees to getting vaccinated.
Other comments:
By applying tooltip, the fourth and fifth visualisation provide audiences with a more comprehensive understanding of the survey results on vaccine willingness at the country level. Similarly, being able to filter via gender and employment status gives insights on the different behaviours within each group.
4. Data Visualisation Step-by-Step